Predictive contextual transaction scoring

ABSTRACT

In one embodiment, a predictive contextual transaction scoring system receives transaction review data associated with person-to-person transactions from a marketplace, the transaction review data including feedback information relating to the person-to-person transaction. The system identifies contextual characteristics of the person-to-person transactions by analyzing the feedback information related to the person-to-person transactions. The system pairs a first party requesting a service to a second party providing the requested service by matching a contextual characteristic of the first party to a contextual characteristic of the second party. The system also predicts a transaction score for a new person-to-person transaction involving the first party and the second party, and provides the predicted transaction score to the marketplace to facilitate pairing of the first party requesting the service.

TECHNICAL FIELD

The present disclosure relates generally to data processing systems, andmore particularly, to prediction of a person-to-person transaction scorebased on identification of contextual characteristics and contextualpreferences derived from prior transaction review data.

BACKGROUND

Internet marketplaces and platforms that rely on user collaborationtypically implement a review system to solicit feedback associated withtransactions or interactions occurring within such marketplaces orplatforms. For marketplaces and platforms that facilitateperson-to-person transactions—such as lodging, transportation, shipping,housework, or other goods and services—it is desired that thetransacting parties each experience a positive, pleasant and successfultransaction. Often, however, due to the nature of person-to-persontransactions, the parties may part ways unsatisfied due to a number ofissues regarding the transaction. For example, a customer who places ahigh priority on a clean vehicle for a rideshare service, may beunsatisfied with a rideshare transaction where a vehicle may be dirtybut otherwise arrives at their intended destination in a timely and safemanner. Unsatisfied parties to a transaction may then leave negativefeedback or reviews, and may even stop using a marketplace or platformaltogether. Existing marketplaces and platforms may implement certainmatching algorithms to match a customer to a provider based on, forexample, a location of a provider, distance from a provider to acustomer, or other non-contextual parameters, but lack consideration ofcontext-related parameters that may contribute to a positive outcome ofa person-to-person transaction.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identical or functionally similarelements. Understanding that these drawings depict only exemplaryembodiments of the disclosure and are not therefore to be considered tobe limiting of its scope, the principles herein are described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 illustrates a schematic diagram of an example network environmentutilizing a predictive contextual transaction scoring system;

FIG. 2 illustrates a schematic diagram of an example predictivecontextual transaction scoring device;

FIG. 3 illustrates a conceptual block diagram of the predictivecontextual transaction scoring system shown in FIG. 1;

FIG. 4 illustrates exemplary data mapping of the contextualcharacteristics data;

FIG. 5 illustrates an example process diagram for predicting acontextual transaction score; and

FIG. 6 illustrates a method for predicting a contextual transactionscore.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

According to one or more embodiments of the disclosure, a predictivecontextual transaction scoring system receives transaction review dataassociated with person-to-person transactions from a marketplace. Thesystem further identifies contextual characteristics of theperson-to-person transactions by analyzing the feedback informationrelated to the person-to-person transactions. The system also pairs afirst party requesting a service to a second party providing therequested service by matching a contextual characteristic of the firstparty to a contextual characteristic of the second party. The systemfurther predicts a transaction score for a new person-to-persontransaction involving the first party and the second party, thepredicted transaction score representing predicted feedback for the newperson-to-person transaction and generated based on the matchedcontextual characteristic of the first party to the second party. Thesystem provides the predicted transaction score to the marketplace tofacilitate pairing of the first party requesting the service. These andother features will be discussed herein with respect to variousexemplary embodiments of the disclosed predictive contextual transactionscoring system.

Description

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.

A predictive contextual transaction scoring environment typicallyincludes a communication network, which is a geographically distributedcollection of devices or nodes interconnected by communication links andsegments for transporting data there-between. The devices may include,for example, electronic devices such as personal computers, laptops,tablets, mobile phones, servers, data centers, and the like. Many typesof networks are available, ranging from local area networks (LANs) towide area networks (WANs). LANs typically connect these nodes overdedicated private communications links located in the same generalphysical location, such as a building or campus. WANs, on the otherhand, typically connect geographically dispersed nodes overlong-distance communications links, such as common carrier telephonelines, optical lightpaths, synchronous optical networks (SONET),synchronous digital hierarchy (SDH) links, etc.

Referring to the figures, FIG. 1 illustrates a schematic diagram of anexample network environment 100 utilizing a predictive contextualtransaction scoring system 110. Communication network 120 is shown forpurposes of illustration and represents various types of networks,including local area networks (LANs), wide area networks (WANs),telecommunication networks (e.g., 4G, 5G, etc.), satellite networks,broadband networks (BBN), and/or a network of networks, such as theInternet, etc. Further, the communication network 120 can include, butis not limited to, any one or more of the following network topologies,including a bus network, a star network, a ring network, a mesh network,a star-bus network, tree or hierarchical network, and the like.

The predictive contextual transaction scoring system 110 is connectedvia the communication network 120 to a plurality of marketplaces 130A-N(collectively marketplaces 130) that facilitate person-to-persontransactions, such as for example, ride sharing platforms, short-termrental platforms, secondary market platforms, etc. Client devices 140A-N(collectively client devices 140) may access the marketplaces 130directly via the communication network 120. As shown, client devices 140include a computer 140A, a mobile device 140B, and a tablet 140C. Theillustrated client devices 140 show specific electronic devices, but itis appreciated that client devices 140 in the broader sense are notlimited to such specific electronic devices. For example, client devices140 can include any number of electronic devices such as laptops, smartwatches, wearable smart devices, smart glasses, vehicle systems, othersmart wearables, and so on.

The marketplaces 130 and client devices 140 are interconnected bycommunication links and/or network segments and exchange or transportdata such as data packets to/from the predictive contextual transactionscoring system 110. In addition, those skilled in the art willunderstand that any number of devices and links may be used incommunication network 120, and that the views shown by FIG. 1 is forsimplicity and discussion.

Data packets sent from marketplaces 130 and/or client devices 140represent network traffic or messages, which are exchanged betweenmarketplaces 130, client devices 140 and predictive contextualtransaction scoring system 110 over communication network 120 usingpredefined network communication protocols such as wired protocols,wireless protocols (e.g., IEEE Std. 802.15.4, WiFi, Bluetooth, etc.),PLC protocols, or other shared-media protocols where appropriate. Inthis context, a protocol consists of a set of rules defining how devicesor nodes interact with each other.

The predictive contextual transaction scoring system 110 includes one ormore machine-readable instructions, and may comprise one or more serversconnected via the communication network 120. In some example aspects,the predictive contextual transaction scoring system 110 can be a singlecomputing device or in other embodiments, the predictive contextualtransaction scoring system 110 can represent more than one computingdevice working together (e.g., in a cloud computing configuration).

FIG. 2 is a schematic block diagram of an example node/device 200 thatmay be used with one or more embodiments described herein, e.g., as acomponent device of the predictive contextual transaction scoring system110 shown in FIG. 1. Device 200 comprises one or more network interfaces210 (e.g., wired, wireless, PLC, etc.), at least one processor 220, anda memory 240 interconnected by a system bus 250, as well as a powersupply 260 (e.g., battery, plug-in, etc.).

Network interface(s) 210 contain the mechanical, electrical, andsignaling circuitry for communicating data over the communication linkscoupled to communication network 120. Network interfaces 210 may beconfigured to transmit and/or receive data using a variety of differentcommunication protocols. Network interface 210 is shown for simplicity,and it is appreciated that such interface may represent two differenttypes of network connections, e.g., wireless and wired/physicalconnections. Also, while network interface 210 is shown separately frompower supply 260, for PLC the interface may communicate through powersupply 260, or may be an integral component of the power supply. In somespecific configurations the PLC signal may be coupled to the power linefeeding into the power supply.

Memory 240 comprises a plurality of storage locations that areaddressable by processor 220 and network interfaces 210 for storingsoftware programs and data structures associated with the embodimentsdescribed herein. Note that certain devices may have limited memory orno memory (e.g., no memory for storage other than for programs/processesoperating on the device and associated caches).

Processor 220 may comprise hardware elements or hardware logic adaptedto execute the software programs (e.g., instructions) and manipulatedata structures 245. An operating system 242, portions of which aretypically resident in memory 240 and executed by the processor,functionally organizes the device 200 by, among other things, invokingoperations in support of software processes and/or services executing onthe device. These software processes and/or services may comprisepredictive contextual transaction scoring process/services 244, asdescribed herein. Note that while predictive contextual transactionscoring process/services 244 is shown in centralized memory 240,alternative embodiments provide for the process to be specificallyoperated within the network interfaces 210, such as a component of a MAClayer, and/or as part of a distributed computing network environment.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules or engines configured to operate in accordance withthe techniques herein (e.g., according to the functionality of a similarprocess). In general, the term module or engine refers to model or anorganization of interrelated software components/functions. Further,while the predictive contextual transaction scoring process 244 is shownas a standalone process, those skilled in the art will appreciate thatthis process may be executed as a routine or module within otherprocesses.

As noted above, parties transacting on person-to-person marketplacesoften leave feedback relating to their experiences on such marketplaces.While such feedback is intended to aid future transacting parties inassessing whether to proceed with a particular transaction, quite oftenthere are situations where a particular preference or contextualcharacteristic of a transacting party may outweigh other factorsrelating to the transaction and cause the transacting party to leave thetransaction feeling unimpressed or dissatisfied. For example, a customerwho places a high priority on a clean vehicle for a ride, may beunsatisfied with a rideshare transaction where a vehicle may be dirtybut otherwise arrives at their intended destination in a timely and safemanner. Unsatisfied parties to a transaction may then leave negativefeedback or reviews, and may even stop using a marketplace or platformaltogether. Existing marketplaces and platforms may implement certainmatching algorithms to match a customer to a provider based on, forexample, a location of a provider, distance from a provider to acustomer, or other non-contextual parameters, but lack context-relatedparameters that may enable or facilitate a positive outcome to atransaction.

Accordingly, there is a need for a system that analyzes historicaltransaction review data to identify contextual characteristics of priortransactions to enable improved matching of a provider to a particularcustomer based on contextual characteristics and preferences of thetransacting parties, as well as the contextual characteristics of acontemplated person-to-person transaction. The disclosed technologyaddresses the foregoing limitations of conventional systems byidentifying contextual characteristics of parties and prior transactionsfrom prior transaction review data, assigning the contextualcharacteristics to the transacting parties, and pairing transactingparties based in part on a match of contextual characteristics andpreferences that are identified as notable or imperative to a partyrequesting a service or product. By considering certain contextualcharacteristics in pairing parties for a transaction, the system mayfurther predict a transaction score for the contemplated pairing. Shouldthe predicted transaction score exceed a baseline or not surpass apredetermined threshold, the contemplated pairing of the parties may beprovided as a recommendation to the appropriate marketplace tofacilitate the completion of the transaction.

These processes are embodied by a contextual transaction scoring system,which includes various systems, devices, and methods discussed herein.At a high level, the platform employs data processing and analysis ofprior transaction review data to identify and catalog contextualcharacteristics of parties and prior transactions, analyzes newtransaction requests to identify an identity and contextualcharacteristic associated with a party requesting a service or product,identifies potential of parties for a person-to-person transaction basedon a match of contextual characteristics, and predicts a transactionscore for the contemplated transaction to improve the likelihood that aparticular transaction will result in satisfied parties on both sides ofthe transaction.

FIG. 3 illustrates a conceptual block diagram 300 of the predictivecontextual transaction scoring system 110 shown in FIG. 1. As shown,predictive contextual transaction scoring system 110 includes one ormore machine-readable instructions, such as a contextual characteristicsmodule 305, a pairing engine 310, and a transaction score predictor 315.The predictive contextual transaction scoring system 110 includes atleast one processor, a memory, and communications capability forreceiving transaction request data 320 and transaction review data 330.

In operation, contextual characteristics module 305 receives transactionreview data 330 from one or more marketplaces 130A-N over thecommunication network 120 (as shown in FIG. 1). The contextualcharacteristics module 305 may utilize APIs to receive the transactionreview data 330. In one aspect, the transaction review data 330 may bereceived periodically or in real-time, as desired. Transaction reviewdata 330 is associated with prior person-to-person transactions thatoccurred in one or more marketplaces 130A-N.

In one example, transaction review data 330 may include party identitydata 332 of those engaged in prior person-to-person transactions (e.g.,username, user identifier, email, social media accounts, etc.); roledata 334 reflecting the roles of the parties engaged in priorperson-to-person transactions (e.g., seller, provider, buyer, orconsumer); service or product data 336 reflecting the product sold,service used, property leased, rented or purchased; marketplace data 338identifying the particular marketplace 130A-N in which the priorperson-to-person transaction occurred; feedback information 340 relatingto the prior person-to-person transaction reflecting a review, rating orexperience of the transacting parties, and may include transactiondetails such as the date, time, amount, and location of the priorperson-to-person transaction. The feedback information 340 may comprisetext, tags, or other data fields that enable searching and identifyingof trends, characteristics, patterns, or subject to data analyticsprocessing to identify contextual characteristics of the priorperson-to-person transactions, as discussed further below. Thetransaction review data 330 may be encrypted or otherwise protected fromexposure to protect sensitive information, such as names, addresses, andpersonal identifying information.

The contextual characteristics module 305 is configured to analyze thefeedback information 340 related to the prior person-to-persontransactions to identify contextual characteristics of the transactingparties and of the prior person-to-person transactions. Specifically,the contextual characteristics module 305 may conduct data analytics onthe feedback information 340 to identify patterns or trends in thefeedback information 340 that are indicative of a particular party'spreference or priorities for certain contextual characteristics that mayaffect or impact the experience of that party in a futureperson-to-person transaction, and further identifies contextualcharacteristics that are associated with a particular type oftransaction (e.g., rideshare transaction, home sharing transaction,secondary market purchase, etc.). The data analytics may involveapplication of text or tag searches, predictive analytics, artificialintelligence (AI) or machine learning algorithms to identify contextualcharacteristics of the transacting parties and of the priorperson-to-person transactions.

For example, for a prior transaction involving a rideshare, arequestor's or rider's feedback information 340 may be analyzed toidentify preferences or priorities for certain contextualcharacteristics associated with the rider, such as a rider's preferencefor a clean vehicle, safe driving, communication style (e.g., quiet ortalkative), desire for amenities (e.g., free water, charging cable,etc.), route selection (e.g., knowledge of different routes, trafficpatterns, shortcuts, over-reliance of navigation applications or GPS,etc.) and timeliness in trip duration. For the providers or drivers, thefeedback information 340 may be analyzed to identify contextualcharacteristics that may be commonly attributed to the drivers, such asdata indicating cleanliness of their car, safe driving, communicationstyle (e.g., quiet of talkative), provision of amenities, knowledge ofalternative routes, or timely arrival. As another example, for priortransactions involving vacation rentals, a requestor's or renter'sfeedback information 340 may be analyzed to identify preferences orpriorities for certain contextual characteristics associated with therenter, such as a renter's dislike for inaccurate descriptions orpictures, preference of clean rentals, effort involved when vacating theproperty, quality of furnishings, timely return of security deposit,desire for personalization (e.g. catered or generic accommodations),local knowledge (e.g., concierge capabilities, recommendations to localattractions or activities) or appreciation for responsiveness. For theprovider or landlord, the feedback information 340 may be analyzed toidentify contextual characteristics that may be commonly attributed tothe landlord, such as data indicating responsiveness, accuracy indescribing the property, personalization of accommodations, or extensiveknowledge of the local community.

The contextual characteristics module 305 may also analyze the feedbackinformation 340 to identify prioritized contextual characteristics thatmay greatly affect or impact the outcome of a particular transaction.For example, analysis of the feedback information 340 may reveal aparty's propensity to place cleanliness above safety for a ridesharetransaction, or a party's propensity to place a sense of safety as themost paramount factor in determining whether the rideshare transactionresults in a positive or negative experience for that party.

The contextual characteristics module 305 may also analyze the feedbackinformation 340 to identify contextual characteristics of priorperson-to-person transactions to determine which contextualcharacteristics appear relevant to a particular transaction or platform.For example, analysis of the feedback information 340 may reveal thatfor a rideshare transaction, contextual characteristics that appearrelevant include safety, cleanliness, amenities, and timeliness. Asanother example, analysis of the feedback information 340 may revealthat for a vacation rental transaction, contextual characteristics thatappear relevant include cleanliness, transparency, and personalization.

Once contextual characteristics and preferences for a particular partyare identified by the contextual characteristics module 305, thecontextual characteristics are assigned to each party to enable pairingby the pairing engine 310. In one example, the contextualcharacteristics may be assigned to the parties by using the partyidentity data 332 and role data 334 as an index. In another example, thecontextual characteristics may be assigned to the parties based onidentity data 332, role data 334, and marketplace data 338. In oneaspect, the contextual characteristics may be stored in a database thatis configured to be queried based on one or more fields provided bytransaction request data 320, as discussed further below. For example,the contextual characteristics module 305 may generate contextualcharacteristics data 350 that includes the contextual characteristicsand/or contextual preferences 355 identified for each party 352, and acorresponding marketplace 358 from which the contextual characteristics355 were derived, as well as the role 354 that the party had in theprior person-to-person transaction. Other data structures ormethodologies may be employed to assign the contextual characteristicsto the parties as would be understood by a person of ordinary skill inthe art.

The pairing engine 310 is configured to pair a party requesting aproduct or service in connection with a new person-to-persontransaction, to a party providing the requested service or product bymatching a contextual characteristic of the requesting party to acontextual characteristic of the providing party. By consideringcontextual characteristics that may be notable or of importance to therequesting party in finding a suitable provider, the likelihood that thetransaction will leave the parties satisfied and content is greatlyincreased.

In operation, the pairing engine 310 receives transaction request data320 reflecting a request for a new person-to-person transaction. Thetransaction request data 320 includes marketplace data 328 identifyingthe particular marketplace 130A-N in which the new person-to-persontransaction request is generated; party identity data 322 identifyingthe requesting party (e.g., username, user identifier, email, socialmedia accounts, etc.); service or product data 326 reflecting theproduct or service requested by the requesting party; and may furtherinclude transaction details such as the date, time, amount, and locationof the request.

In one example, the marketplace for the new person-to-person transactionis identified from the marketplace data 328. Based on the identifiedmarketplace, relevant or applicable contextual characteristics for thenew person-to-person transaction are identified for use in a pairingoperation. A search for the relevant contextual characteristics of therequesting party may then be conducted using the party identity data 322from the transaction request data 320 to determine whether therequesting party has contextual characteristics that have beenpreviously identified by the contextual characteristics module 305. Inthis example, the pairing engine 310 may search the contextualcharacteristics data 350 for the relevant or applicable contextualcharacteristics 355 of the requesting party by looking for a matchbetween the party identity data 322 from the transaction request data320 and the party identity data 352 in the contextual characteristicsdata 350. In another example, the contextual characteristics module 305may search the party identity data 332 of the transaction review data330 using the party identity data 322 from the transaction request data320 to identify feedback information 340 corresponding to the requestingparty. Once the feedback information 340 is found, the contextualcharacteristics module 305 may perform data analytics, as describedabove, to identify contextual characteristics from the feedbackinformation 340 associated with the requesting party.

In one example, once the contextual characteristics 355 of therequesting party are identified, the pairing engine 310 may search thecontextual characteristics data 350 for potential providers 352 byidentifying parties who have roles 354 as providers of the product orservice 356 requested by the requesting party, as may be identified fromthe service or product data 326, and who have transacted in the samemarketplace 358 as identified in the marketplace data 328. In anotherexample, the pairing engine 310 may search the transaction review data330 for potential providers of the requested product or service byidentifying parties 332 who have roles 334 as providers of the requestedproduct or service by reviewing the service or product data 336 andmarketplace data 338. In some aspects, the pairing engine 310 may firstreceive an initial list of provider candidates from the marketplacereflecting providers that may be currently offering the requestedproduct or service. From this list of initial candidates, the pairingengine 310 may identify viable providers of the requested product orservice to ensure that the resulting recommended pairing includesproviders that are indeed available to offer the requested product orservice.

Specifically, the pairing engine 310 may first query the marketplacedata 338 of the transaction review data 330 to identify parties who havetransacted in the same marketplace as the marketplace 328 identified inthe transaction review data 330. Once potential parties are identified,the pairing engine 310 may further query the role data 334 of thetransaction review data 330 to identify those parties who have had arole as a provider. Once these potential providers have been identified,the pairing engine 310 may further query the service or product data 336of the transaction review data 330 to identify those parties who havepreviously provided similar goods or services as the product or service326 identified in the transaction request data 320. Once the potentialproviders that have previously provided similar goods or services in thesame marketplace are identified, the pairing engine may utilize theresulting party data 332 from the transaction review data 330 to querythe party data 352 of the contextual characteristics data 350, toidentify the corresponding relevant or applicable contextualcharacteristics 355 of the potential providers. Once the contextualcharacteristics 355 of the potential providers are identified, thepairing engine 310 may look for a match between the contextualcharacteristics 355 of the requesting party and the contextualcharacteristics 355 of the providing parties.

For example, should a requesting party be a rider requesting arideshare, analysis of the rider's feedback information may revealcontextual characteristics reflecting that the rider values a cleanvehicle and safe driving. The pairing engine 310 will search for drivershaving contextual characteristics, derived from feedback informationfrom prior person-to-person transactions, that indicates cleanliness andsafe driving. By matching drivers with such attributes to preferencesdesired by the rider, it is more likely that both parties will completethe transaction satisfied and pleased, resulting in positive feedbackand reviews for both the rider and driver.

The transaction score predictor 315 is configured to predict a“transaction score” resulting from the proposed pairing of therequesting party and the providing party. In other words, the“transaction score” represents a predicted feedback, grade or reviewthat is expected from the requesting party 322 as a result of thecontemplated transaction with the providing party. In one aspect, thepredicted transaction score may be based on a multitude of contextualcharacteristics and preferences 355 of the transacting parties, such asa party's propensity to provide more positive reviews in certain timesof the day, certain days of the week, for short trips versus longertrips, for services consumed locally versus away from home, for ridesprovided in higher-end luxury vehicles, or other contextual factors thatmay be derived from analyzing the feedback information of the parties.The transaction score is thus dynamic in that it may change based on theparticular contextual characteristics of the parties involved, as wellas contextual characteristics of the transaction itself (e.g., time ofday, duration of service, location of service, cost of service, etc).For example, the predicted transaction score may change based on thetime of day, type of transaction, a party's role in the transaction,and/or preferences of the transacting party.

The predicted transaction score may be used to determine whether anotherpairing should be recommended where the initial predicted transactionscore is lower than an acceptable value or threshold. For example,should a particular marketplace require a predicted transaction score of4 out of 5 (in a 5 star scale), and a first proposed pairing results ina 3-star predicted score based on the fact that the requesting partytends to score lower in the early mornings when riding in a smallvehicle, the pairing engine 310 may recommend a different pairing with aprovider offering a larger vehicle (e.g., SUV). The transaction scorepredictor 315 may then generate a predicted transaction score with thelarger vehicle as a contextual consideration that may result in a highertransaction score of 4-stars. In this example, the pairing with thesecond provider having the larger vehicle is more desirable as there isa higher likelihood that the parties involved in the contemplatedperson-to-person transaction will be satisfied and pleased with thetransaction.

Once a suggested pairing results in an acceptable predicted transactionscore, the recommended pairing may be conveyed or provided to themarketplace 328 identified in the transaction request data 320 tofacilitate pairing of the requesting party 322 with the recommendedproviding party.

FIG. 4 illustrates exemplary data mapping of the contextualcharacteristics data 350. In particular, the contextual characteristicsdata 350 data is mapped to the parties 352 so that for each party 352,contextual characteristics and preferences 355 and priorities 430 areidentified, along with the party's respective role 354 in the priorperson-to-person transactions, the services and/or products 356 consumedor provided by the party, and the marketplaces 358 in which the partyhas consummated prior person-to-person transactions.

For example, as shown in FIG. 4, a first party 352A has contextualcharacteristics 355 of safety 421 as their highest priority 430,cleanliness 422 as their second highest priority 430, and timeliness 423as their third highest priority. The first party has a role 354 of riderand has previously consumed rideshare services 356 on a rideshareplatform 358. Upon a request for a new rideshare transaction on the samerideshare platform or a similar alternative, the pairing engine 310 (asshown in FIG. 3) searches the contextual characteristics data 350 for apairing party. The pairing engine 310 may first search for parties thathave previously transacted on the same or similar rideshare platform.Here, parties 352B and 352D have previously transacted on the rideshareplatform 358. Next, the pairing engine 310 may search for parties whoare drivers 354 to identify potential candidates for pairing with thefirst party 352A. Again, the second party 352B and fourth party 352Dremain viable candidates for pairing. The third party 352C is not acandidate because third party 352C has no history of transacting in anyrideshare platform, having instead transacted on a rental platform 358as a host 354 in providing a short-term rental.

The pairing engine 310 may then search the contextual characteristics355 of the second party 352B and the fourth party 352D to determinewhether there is a match with the contextual characteristics of thefirst party 352A. Here, the first party 352A places safety 421 as themost important contextual characteristic thereby resulting in the secondparty 352B, who while perhaps friendly 425, is a poor candidate due tothe second party's propensity to drive above the speed limit 424. Inlooking at the contextual characteristics 355 of the fourth party 352D,there is a match in that the fourth party 352D has been identified as adriver with a high safety 421 rating or ranking. While the fourth party352D may be inexperienced 426, the contextual characteristics of thefirst party 352A do not indicate that experience is a contextualcharacteristic 355 that may negatively impact a rating of aperson-to-person transaction involving inexperienced service providers.

FIG. 5 illustrates an example process diagram 500 for predicting acontextual transaction score. It should be understood that, for anyprocess discussed herein, there can be additional, fewer, or alternativesteps performed in similar or alternative orders, or in parallel, withinthe scope of the various aspects unless otherwise stated. The process500 can be performed by the network environment 100 utilizing apredictive contextual transaction scoring system 110.

At step 502, transaction review data including feedback informationrelating to prior person-to-person transactions is received from one ormore marketplaces. The transaction review data may also include theidentity of parties involved in the prior person-to-person transactions,and a role of the parties in the prior person-to-person transactions. Atstep 504, contextual characteristics are identified by analyzing thefeedback information related to the prior person-to-person transactions.At step 506, the identified contextual characteristics and preferencesare assigned to the corresponding parties in the prior person-to-persontransactions. At step 508, a request for a new person-to-persontransaction request is received. The new transaction request may includeinformation identifying the requesting party. At step 510, therequesting party and the contextual characteristics attributable to therequesting party are identified. In one example, the transaction reviewdata may be queried by using the identity information of the requestingparty.

At step 512, a search for potential candidates to pair with therequesting party is performed. In one aspect, the search may considerthe prior roles and services or goods offered by the candidates. At step514, it is determined whether there is a match between the contextualcharacteristics of the requesting party and the contextualcharacteristics of a potential providing party. If there is no match,then a search for additional candidates is conducted. If there is amatch, then at step 516, the requesting party and the providing partyare paired. In some aspects, pairing of parties may require matching ofmore than one contextual characteristics. At step 518, a transactionscore is predicted for the contemplated new person-to-person transactioninvolving the requesting party and the providing party. The predictedtransaction score represents predicted feedback for the newperson-to-person transaction and is generated based on a match of atleast one contextual characteristic of the first party to the secondparty.

At step 520, it is determined whether the predicted transaction scoreexceeds a predetermined threshold. If not, then a new candidate searchis conducted at step 512. In this case, the requesting party would bepaired with a different party providing the requested service bymatching one or more contextual characteristics of the requesting partyto the contextual characteristics of the different party. If there was amatch between the contextual characteristics of the requesting party andthe different party, then a second transaction score would be predictedfor the contemplated transaction involving the requesting party and thedifferent party. If the predicted transaction score exceeds thepre-determined threshold or is otherwise acceptable, then at step 522,the recommended pairing is provided to the marketplace to facilitatepairing of the requesting party to the recommended providing party.

FIG. 6 illustrates a method 600 for predicting a contextual transactionscore in accordance with the processes described above. For purposes ofdiscussion, the operations for procedure 600 are described in thecontext of the predictive contextual transaction scoring system 110 orsimply the “system.” As described above, the system identifiescontextual characteristics of transacting parties to recommend a pairingand generates a predicted transaction score in order to increase thelikelihood that the recommended pairing will result in a positivetransaction experience for all parties involved in a new contemplatedperson-to-person transaction.

The procedure 600 begins at step 602, where, as described in greaterdetail above, the system receives transaction review data associatedwith prior person-to-person transactions from one or more marketplaces.The transaction review data includes feedback information relating tothe prior person-to-person transactions, and may further include anidentity of parties involved in the prior person-to-person transactions,and a role of the parties in the prior person-to-person transactions.

Procedure continues to step 604, where the system identifies contextualcharacteristics of the prior person-to-person transactions by analyzingthe feedback information related to the prior person-to-persontransactions. Specifically, data analytics may be performed on thefeedback information to identify patterns or trends in the feedbackinformation that are indicative of a particular party's preference orpriorities for certain contextual characteristics that may affect orimpact the experience of that party in a future person-to-persontransaction, and further identifies contextual characteristics that areassociated with a particular type of transaction (e.g., ridesharetransaction, home sharing transaction, secondary market purchase, etc.).The data analytics may involve application of text or tag searches,predictive analytics, artificial intelligence (AI) or machine learningalgorithms to identify contextual characteristics of the transactingparties and of the prior person-to-person transactions.

At step 606, the identified contextual characteristics and preferencesare assigned to the parties involved in the person-to-persontransactions. In one example, the contextual characteristics andpreferences may be assigned to the parties by using an index. In oneaspect, the contextual characteristics and preferences may be stored ina database that is configured to be queried based on one or more fields.At step 608, a request for a new person-to-person transaction isreceived. The new person-to-person request may include an identity of afirst requesting party, the requested service or goods, and theoriginating marketplace. At step 610, the transaction review data may besearched for the identity of the first requesting party. Onceidentified, the contextual characteristics of the first requesting partymay be identified. At step 612, a search for a second providing party isperformed on the transaction review data based on the role of the secondproviding party. In some aspects, an initial list of provider candidatesmay be received from the originating marketplace reflecting providersthat may be currently offering the requested product or service. Fromthis list of initial candidates, viable providers of the requestedproduct or service may be identified to ensure that the resultingrecommended pairing includes providers that are indeed available tooffer the requested product or service.

At step 614, the first requesting party is paired to the second partyproviding the requested service by matching one or more contextualcharacteristics of the first requesting party to a contextualcharacteristics of the second party, as well as considering thepriorities of such matching contextual preferences and characteristics.At step 616, a transaction score is predicted for the newperson-to-person transaction involving the first party and the secondparty. The predicted transaction score represents a predicted feedbackfor the new person-to-person transaction and is generated based on thematched contextual characteristics of the first party to the secondparty. Should a predicted transaction score fall below an acceptablelevel or threshold, the first party requesting the service may be pairedto a third party providing the requested service by matching one or morecontextual characteristics of the first party to the contextualcharacteristics of the third party, as well as by considering thepriorities of such matching contextual characteristics. A secondpredicted transaction score for the contemplated new person-to-persontransaction involving the first party and the third party is generated,the second predicted transaction score representing predicted feedbackfor the contemplated new person-to-person transaction and generatedbased on the matching contextual characteristics of the first party andthe third party.

Procedure 600 subsequently ends at step 618, where the predictedtransaction score is provided to the marketplace to facilitate pairingof the first party requesting the service, but may continue on to step602 where, as discussed above, system receives transaction review dataassociated with prior person-to-person transactions from one or moremarketplaces, or at step 608 where a request for a new person-to-persontransaction is received. It should be noted that certain steps withinprocedure 600 may be optional, and further, the steps shown in FIG. 6are merely examples for illustration—certain other steps may be includedor excluded as desired. Further, while a particular order of the stepsis shown, this ordering is merely illustrative and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, describe identification ofcontextual preferences and characteristics of transacting parties torecommend a pairing and generation of a predicted transaction score inorder to increase the likelihood that the recommended pairing willresult in a positive transaction experience for all parties involved inany newly contemplated person-to-person transaction. The processes andmethods described herein analyze prior feedback information to identifycontextual characteristics that may impact an outcome of aperson-to-person transaction.

While there have been shown and described illustrative embodiments of apredictive contextual transaction scoring system, it is to be understoodthat various other adaptations and modifications may be made within thespirit and scope of the embodiments herein. For example, the embodimentshave been shown and described herein with relation to a specific systemthat organizes certain functions into modules or engines. However, theembodiments in their broader sense are not as limited, and may, in fact,be used with any number of applications, devices, and systems as part ofa distributed computing network.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium, devices, and memories (e.g., disks/CDs/RAM/EEPROM/etc.) havingprogram instructions executing on a computer, hardware, firmware, or acombination thereof. Further, methods describing the various functionsand techniques described herein can be implemented usingcomputer-executable instructions that are stored or otherwise availablefrom computer readable media. Such instructions can comprise, forexample, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on. In addition, devices implementingmethods according to these disclosures can comprise hardware, firmwareand/or software, and can take any of a variety of form factors. Typicalexamples of such form factors include laptops, smart phones, small formfactor personal computers, personal digital assistants, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example. Instructions, media for conveyingsuch instructions, computing resources for executing them, and otherstructures for supporting such computing resources are means forproviding the functions described in these disclosures. Accordingly thisdescription is to be taken only by way of example and not to otherwiselimit the scope of the embodiments herein. Therefore, it is the objectof the appended claims to cover all such variations and modifications ascome within the true spirit and scope of the embodiments herein.

1. A predictive contextual transaction scoring system, comprising: oneor more network interfaces to communicate over a network; a processorcoupled to the network interfaces and adapted to execute one or moreprocesses; and a memory having instructions executable by the processorencoded thereon, the instructions, when executed, cause the processorto: receive, by the processor, transaction review data associated withperson-to-person transactions from a marketplace, the transaction reviewdata including a transaction type and feedback information relating toeach respective person-to-person transaction of the person-to-persontransactions; identify, by the processor, first contextualcharacteristics for a first person based on the feedback informationassociated with the first person for a particular transaction type;update, by the processor, unique preferences associated with the firstperson and the particular transaction type based on the first contextualcharacteristics; prioritize, by the processor, the unique preferencesbased on the feedback information to create prioritized preferences;receive, by the processor and over the network, a request from a firstdevice associated with the first person corresponding to servicesassociated with the particular transaction type in the marketplace;pair, by the processor, the first person with a second person thatprovides the services associated with the particular transaction typebased on the prioritized preferences and second contextualcharacteristics associated with the second person, the second contextualcharacteristics including feedback information associated withperson-to-person transactions involving the second person for theparticular transaction type; predict, by the processor, a transactionscore for a potential person-to-person transaction involving the firstperson and the second person based on a match between the prioritizedpreferences and the second contextual characteristics for the particulartransaction type, the predicted transaction score representing predictedfeedback for the potential person-to-person transaction; and facilitate,by the processor and over the network, a new person-to-persontransaction involving the first person and the second person providingthe services associated with the particular transaction type based onthe predicted transaction score.
 2. (canceled)
 3. The predictivecontextual transaction scoring system of claim 1, wherein thetransaction review data further includes an identifier associated withpersons involved in the person-to-person transactions, and a roleassociated with the identifier.
 4. The predictive contextual transactionscoring system of claim 3, wherein the request for the first personincludes a first identifier associated with the first person, whereinthe instructions, when executed by the processor, further cause theprocessor to: search, by the processor, the transaction review data fora second identifier associated with the second person based on a roleassociated with the second person; and identify, by the processor, thesecond contextual characteristics of the second person based on thesecond identifier of the second person.
 5. The predictive contextualtransaction scoring system of claim 1, wherein the instructions, whenexecuted by the processor, further cause the processor to: pair, by theprocessor, the first person with a third person that provides theservices associated with the particular transaction type based on theprioritized preferences and third contextual characteristics associatedwith the third person, the third contextual characteristics includingfeedback information associated with person-to-person transactionsprovided by the third person for the particular transaction type; andpredict, by the processor, a second transaction score for a secondpotential person-to-person transaction involving the first person andthe third person, based on a match between the prioritized preferencesand the third contextual characteristics, the second predictedtransaction score representing predicted feedback for the secondpotential person-to-person transaction.
 6. The predictive contextualtransaction scoring system of claim 5, wherein the instructions, whenexecuted by the processor, further cause the processor to: facilitate,by the processor and over the network, a new person-to-persontransaction involving the first person and the third person providingthe services associated with the particular transaction type based onthe second predicted transaction score.
 7. The predictive contextualtransaction scoring system of claim 1, wherein the first contextualcharacteristics of the first person includes a unique preference of thefirst person for the particular transaction type.
 8. The predictivecontextual transaction scoring system of claim 7, wherein the uniquepreference of the first person comprises at least one of: a cleanlinessrating preference, a safety rating preference, a communication stylepreference, a provision of amenities preference, a personalization ofservices preference, a route selection preference, a local knowledgerating preference, and a timeliness rating preference.
 9. The predictivecontextual transaction scoring system of claim 1, wherein the feedbackinformation comprises at least one of a text field and tag field.
 10. Amethod for predicting a contextual transaction score, the methodcomprising: receiving, by a processor in communication with a memory,transaction review data associated with person-to-person transactionsfrom a marketplace, the transaction review data including a transactiontype and feedback information relating to each respectiveperson-to-person transaction the person-to-person transactions;identifying, by the processor, first contextual characteristics for afirst person based on the feedback information associated with the firstperson for a particular transaction type; updating, by the processor,unique preferences associated with the first person and the particulartransaction type based on the first contextual characteristics;prioritizing, by the processor, the unique preferences based on thefeedback information to create prioritized preferences; receiving, bythe processor and over a network in communication with the processor, arequest from a first device associated with the first personcorresponding to services associated with the particular transactiontype in the marketplace pairing, by the processor, the first person witha second that provides the services associated with the particulartransaction type based on the prioritized preferences and secondcontextual characteristics associated with the second person, the secondcontextual characteristics including feedback information associatedwith person-to-person transactions involving the second person for theparticular transaction type; predicting, by the processor, a transactionscore for a potential person-to-person transaction involving the firstperson and the second person based on a match between the prioritizedpreferences and the second contextual characteristics for the particulartransaction type, the predicted transaction score representing predictedfeedback for the potential person-to-person transaction; andfacilitating, by the processor and over the network, a newperson-to-person transaction involving the first person and the secondperson providing the services associated with the particular transactiontype based on the predicted transaction score.
 11. (canceled)
 12. Themethod of claim 10, further comprising: pairing, by the processor, thefirst person with a third person that provides the services associatedwith the particular transaction type based on the prioritizedpreferences and third contextual characteristics associated with thethird person, the third contextual characteristics including feedbackinformation associated with person-to-person transactions provided bythe third person for the particular transaction type; and predicting, bythe processor, a second transaction score for a second potentialperson-to-person transaction involving the first person and the thirdperson, based on a match between the prioritized preferences and thethird contextual characteristics, the second predicted transaction scorerepresenting predicted feedback for the second potentialperson-to-person transaction.
 13. The method of claim 12, furthercomprising facilitating, by the processor and over the network, a newperson-to-person transaction involving the first person and the thirdperson providing the services associated with the particular transactiontype based on second predicted transaction score.
 14. The method ofclaim 10, wherein the first contextual characteristics of the firstparty includes a unique preference of the first person for theparticular transaction type.
 15. The method of claim 14, wherein theunique preference of the first party comprises at least one of: acleanliness rating preference, a safety rating preference, acommunication style preference, a provision of amenities preference, apersonalization of services preference, a route selection preference, alocal knowledge rating preference, and a timeliness rating preference.16. The method of claim 10, wherein the feedback information comprisesat least one of a text field and tag field.
 17. A tangible,non-transitory, computer-readable media having instructions encodedthereon, the instructions, when executed by a processor, cause theprocessor to: receive, by the processor, transaction review dataassociated with person-to-person transactions from a marketplace, thetransaction review data including a transaction type and feedbackinformation relating to each respective person-to-person transaction ofthe person-to-person transactions; identify, by the processor, firstcontextual characteristics for a first person based on the feedbackinformation associated with the first person for a particulartransaction type; update, by the processor, unique preferencesassociated with the first person and the particular transaction typebased on the first contextual characteristics; prioritize, by theprocessor, the unique preferences based on the feedback information tocreate prioritized preferences; receive, by the processor and over anetwork in communication with the processor, a request from a firstdevice associated with the first person corresponding to servicesassociated with the particular transaction type in the marketplace;pair, by the processor, the first person to a second person thatprovides the services associated with the particular transaction typebased on the prioritized preferences and second contextualcharacteristics associated with the second person the second contextualcharacteristics including feedback information associated withperson-to-person transactions involving the second person for theparticular transaction type; predict, by the processor, a transactionscore for a potential person-to-person transaction involving the firstperson and the second person, based on a match between the prioritizedpreferences and the second contextual characteristics for the particulartransaction type, the predicted transaction score representing predictedfeedback for the potential person-to-person transaction; and facilitate,by the processor and over the network, a new person-to-persontransaction involving the first person and the second person providingthe services associated with the particular transaction type based onthe predicted transaction score.
 18. The tangible, non-transitory,computer-readable media of claim 17, wherein the first contextualcharacteristics of the first person includes a unique preference of thefirst person for the particular transaction type.
 19. The tangible,non-transitory, computer-readable media of claim 18, wherein the uniquepreference of the first person comprises at least one of: a cleanlinessrating preference, a safety rating preference, a communication stylepreference, a provision of amenities preference, a personalization ofservices preference, a route selection preference, a local knowledgerating preference, and a timeliness rating preference.
 20. The tangible,non-transitory, computer-readable media of claim 17, wherein thefeedback information comprises at least one of a text field and tagfield.